Method for computing the sensitivity of a vlsi design to both random and systematic defects using a critical area analysis tool

ABSTRACT

A method of estimating integrated circuit yield comprises providing an integrated circuit layout and a set of systematic defects based on a manufacturing process. Next, the method represents a systematic defect by modifying structures in the integrated circuit layout to create modified structures. More specifically, for short-circuit-causing defects, the method pre-expands the structures when the structures comprise a higher systematic defect sensitivity level, and pre-shrinks the structures when the structures comprise a lower systematic defect sensitivity level. Following this, a critical area analysis is performed on the integrated circuit layout using the modified structures, wherein dot-throwing, geometric expansion, or Voronoi diagrams are used. The method then computes a fault density value, random defects and systematic defects are computed. The fault density value is subsequently compared to a predetermined value, wherein the predetermined value is determined using test structures and/or yield data from a target manufacturing process.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a division of U.S. application Ser. No. 12/348,070,filed Jan. 2, 2009, which is a division of U.S. Pat. No. 7,487,476,Issued Feb. 3, 2009, the complete disclosures of which, in theirentirety, are herein incorporated by reference.

BACKGROUND

1. Field of the Invention

Embodiments of the invention provide a method for computing thesensitivity of a VLSI design to both random and systematic defects usinga critical area analysis tool.

2. Description of the Related Art

In the design and manufacture of very large scale integration (VLSI)semiconductor chips, two types of yield detractors in the manufacturingprocess are random defects and systematic defects. Random defects referto electrical faults (e.g., opens and shorts) caused by foreign materialor impurities. Systematic defects—also known as process-sensitivesites—constitute electrical faults that arise because of the inherentdifficulty of repeatedly building certain structures in a givenmanufacturing technology.

Random defects are predicted using critical area. Critical area is amathematical measure of the sensitivity of a VLSI layout to randomdefects. It is a function of the sizes of features and the spacesbetween features. There are several techniques for computing criticalarea (e.g., dot-throwing, geometric expansion, and Voronoi diagrams).Critical area models the sensitivity of a given manufacturing process topoint defects. In the critical-area model, all shapes allowed in atechnology are assumed to have equal defect sensitivity.

By contrast, systematic defects result because some structures are moredifficult to manufacture than others. In a given process, certaingeometrical configurations can be particularly susceptible to yieldproblems. For example: certain combinations of wide metal lines andnarrow metal lines might interact in a certain way because of the polishstage and lead to electrical shorts and thus non-functional parts. Ingeneral, there is no simple way to measure the sensitivity of a designto systematic defects. One technique uses a design rule check (DRC) tolook for certain configurations and then produce a raw count. While thistechnique provides a means of identifying sites that could cause yieldissues, it does not allow yield to be predicted and does not provide ameans to trade off options to lessen overall combined systematic andrandom yield sensitivity.

In addition there is a random component to systematic-defect analysis: aprocess-sensitive site is not guaranteed to cause an electrical faultbut rather will cause a fault with some probability. An overall yieldestimate for a semiconductor design therefore requires two separateanalyses: critical area for random defects and quasi-DRC for systematicdefects. Furthermore, the systematic-defect analysis should take intoaccount the probabilistic nature of systematic defects. Presently, thereis no good method for performing a unified analysis having thesecharacteristics.

SUMMARY

Embodiments of the invention provide a method for computing thesensitivity of a VLSI design to both random and systematic defects usinga critical area analysis (CAA) tool.

One method of estimating integrated circuit yield comprises providing anintegrated circuit layout and a set of systematic defects based on amanufacturing process. Next, the method represents a systematic defectby modifying structures in the integrated circuit layout to createmodified structures. More specifically, for defects causing a shortcircuit between electrically distinct shapes, the method pre-expands thestructures when the structures comprise a higher systematic defectsensitivity level, and pre-shrinks the structures when the structurescomprise a lower systematic defect sensitivity level. Conversely, fordefects causing an open circuit in a shape, the method pre-shrinks thestructures when the structures comprise a higher systematic defectsensitivity level and pre-expands the structures when the structurescomprise a lower systematic defect sensitivity level.

Following this, a critical area analysis is performed on the integratedcircuit layout using the modified structures, wherein dot-throwing,geometric expansion, or Voronoi diagrams are used. The method thencomputes a fault density value, wherein random defects and systematicdefects are computed. The fault density value is subsequently comparedto a predetermined value, wherein the predetermined value is determinedusing test structures and/or yield data from a target manufacturingprocess.

In other words, the method estimates integrated circuit yield, whereinthe method identifies shape configurations susceptible to systematicdefects and measures the sensitivity of the shape configurations to thesystematic defects. Next, layout edges of the shape configurations arepre-expanded or pre-shrunk based on the sensitivity to the systematicdefects. More specifically, for defects causing a short between twoelectrically distinct shapes, the layout edges are pre-expanded when theshape configurations comprise a higher systematic defect sensitivitylevel, and pre-shrunk when the shape configurations comprise a lowersystematic defect sensitivity level.

A critical area analysis is then performed, comprising computing a faultdensity value. Specifically, dot-throwing, geometric expansion, orVoronoi diagrams are used to perform the critical area analysis.Further, the computing of the fault density value computes randomdefects and systematic defects. Following this, a size of the shapeconfigurations are modified until the fault density value matches apredicted fault density, wherein the predicted fault density isdetermined using test structures and/or yield data from a targetmanufacturing process.

Another method of embodiments herein estimates integrated circuit yield,wherein the method provides an integrated circuit layout, test sitedata, and a set of systematic defects based on the test site data. Next,the method assigns each set of systematic defects a weight based on aprobability of a fault occurring, wherein the weight is derived fromtest structures in technology test sites.

Following this, a critical area analysis is performed on the integratedcircuit layout using the weight, wherein the critical area analysisperforms dot-throwing, geometric expansion, or using Voronoi diagrams.The method then computes a fault density value, wherein random defectsand systematic defects are computed. Subsequently, the fault densityvalue is compared to a predetermined value.

In other words, the method estimates integrated circuit yield, whereinthe method derives weighting factors for geometric configurations in anintegrated circuit, wherein the weighting factors represent increasedprobabilities of a fault occurring at the geometric configurations. Theweighting factors are derived from test structures in technology testsites. Next, one of the weighting factors is assigned to each of thegeometric configurations based on the increased probabilities of thefault occurring at the geometric configurations.

Following this, a critical area analysis is performed using theweighting factors, wherein a fault density value is computed. Thecritical area analysis is performed using dot-throwing, geometricexpansion, or Voronoi diagrams. Moreover, the computing of the faultdensity value computes random defects and systematic defects.

Accordingly, embodiments herein use the basic framework of critical areaanalysis to measure sensitivity to random defects. To account forsystematic defects, the analysis is modified by giving higher weight tothe faults involving geometric configurations that are known to besensitive in the manufacturing process. The computed value from thisprocedure is thus no longer simply critical area but is instead weightedcritical area.

These and other aspects of the embodiments of the invention will bebetter appreciated and understood when considered in conjunction withthe following description and the accompanying drawings. It should beunderstood, however, that the following descriptions, while indicatingpreferred embodiments of the invention and numerous specific detailsthereof, are given by way of illustration and not of limitation. Manychanges and modifications may be made within the scope of theembodiments of the invention without departing from the spirit thereof,and the embodiments of the invention include all such modifications.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the invention will be better understood from thefollowing detailed description with reference to the drawings, in which:

FIG. 1 is a flow diagram illustrating a method of estimating integratedcircuit yield; and

FIG. 2 is a flow diagram illustrating another method of estimatingintegrated circuit yield.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The embodiments of the invention and the various features andadvantageous details thereof are explained more fully with reference tothe non-limiting embodiments that are illustrated in the accompanyingdrawings and detailed in the following description. It should be notedthat the features illustrated in the drawings are not necessarily drawnto scale. Descriptions of well-known components and processingtechniques are omitted so as to not unnecessarily obscure theembodiments of the invention. The examples used herein are intendedmerely to facilitate an understanding of ways in which the embodimentsof the invention may be practiced and to further enable those of skillin the art to practice the embodiments of the invention. Accordingly,the examples should not be construed as limiting the scope of theembodiments of the invention.

Accordingly, embodiments herein use the basic framework of critical areaanalysis to measure sensitivity to random defects. To account forsystematic defects, the analysis is modified by giving higher weight tothe faults involving geometric configurations that are known to besensitive in the manufacturing process. The computed value from thisprocedure is thus no longer simply critical area but is instead weightedcritical area.

As an example, a via near the edge of a wide metal line is particularlydifficult to manufacture in the target manufacturing process because ofvariations in width of wide wires on the die (processes are usuallytuned to eliminate variability in the widths of minimum-width wires). Ifthe wire prints too narrow by twice the size of the via, then the viawould not contact the wire at all, and the circuit could have anelectrical open (assuming there were no redundant via set back from theedge of the wire). Thus, during the calculation for random-defect viaopens, a higher weight is given to vias of this kind (via at the edge ofa wide wire). A standard critical area analysis (CAA) tool is used tocompute critical area for via blocks on the layer in question, but ahigher weight is also assigned to certain vias as described above. Thecomputed weighted critical area measures sensitivity to random defectsand also to systematic defects of the type described. This singleweighted CAA thus produces a value that can be used to obtain a moreaccurate yield estimate and requires only a single analysis of thelayout data.

A standard CAA technique is used to measure sensitivity to randomdefects, using one of the three principal CAA methods: dot-throwing,geometric expansion, or Voronoi diagrams. Two approaches are provided toachieve this goal. The first approach involves expanding and/orshrinking of shapes. To measure sensitivity to systematic defects, shapeconfigurations are identified that are susceptible to process problems.Then, pre-expanding or pre-shrinking of layout edges of the particularshapes corresponding to these sites is conducted and the CAA isperformed. Each of the three main CAA techniques can be adapted in astraightforward manner to compute this weighted critical area.

In a dot-throwing example, minimum-width, minimum-space M3 lines lyingabove wide M2 lines are prone to shorting because of chemical-mechanicalpolish (CMP) problems. To account for this process sensitivity, theedges of these M3 lines are expanded by a small amount where they lieabove the wide M2 wires. Then, CAA is performed to measure M3 shorts.Dots are more likely to cause shorts in the M3 lines that are expanded,which matches the fact that these lines are more sensitive to shorts forreasons other than just critical area.

For the example of vias at the edge of wide lines, the wide lines arepre-shrunk in the neighborhood of such vias. The “opens with via block”critical-area computation then records a higher incidence of dots thatblock these vias in the pre-shrunk lines.

The geometric-expansion technique involves repeated shape expansion ofprescribed sizes. It's then natural to combine the expansion orshrinking of certain shapes with the overall expansion used in thistechnique.

In Voronoi-based methods, the coordinates of each layout edge enter intoan analytic calculation for the critical area of number of disjointpolygons near that edge. The effect of expanding or shrinking an edgecan be brought into this calculation by modifying the edge coordinates.

In each case, the amount by which a shape edge is shrunk or expanded isarrived at by calibration of this technique with yield data from thetarget manufacturing process, using carefully constructed teststructures. If the failure rate for a given systematic defect isreported in parts-per-million (PPM), then the weighted-CAA technique isapplied to the test layout using different expansion/shrink amountsuntil the overall fault density—for random defects plus PPM forsystematic defects—matches the predicted fault density for the testdata.

Therefore, this technique uses a given set of systematic defects for agiven manufacturing process. For each kind of systematic defect, it isdecided how to represent the effect of the defect as an expansion orshrink of specific layout edges (possibly on different layers). Next, aCAA is performed, taking the shrinks and expands into account to computea total fault density number. Following this, the size of the variousexpand/shrink actions is modified until the computed fault densitymatches the predicted fault density for the test layout. The calibratedtechnique is then applied to general layouts.

The second approach involves a weighted CAA. This approach does not relyon shrinking or expanding shapes prior to CAA but rather to assigningweights to certain geometric configurations.

In the case of a dot-throwing approach to CAA, it is known for each dotthe shape(s) for which the defect occurs. As an additional input, theincreased probability of a fault for certain patterns is needed. If, forexample, a short between adjacent minimum and wide wires occurs with ahigher probability, the contribution of this dot to CAA is multiplied bythe appropriate weight. In the case of a Voronoi CAA, a Voronoi polygoninvolving adjacent minimum-width and wide wires is given additionalweight when its critical-area contribution is tallied.

Therefore, this technique is given a set of systematic defects for agiven manufacturing process. Next, a weight is derived that isproportional to the increased probability of a fault for this geometricconfiguration. The standard weight is 1. Weights are derived fromtechnology test sites. Technology test sites contain representativelayouts that are suspected to have systematic-defect sensitivities.Yield of each systematic layout is measured on the technology test site,and a weighting factor is determined for each type of structure.Subsequently, a CAA is performed, using the weighting factors derivedabove, to compute a total fault density number. The calibrated techniqueis then applied to general layouts.

Thus, embodiments of the invention provide a method for computing thesensitivity of a VLSI design to both random and systematic defects usinga critical area analysis tool.

One method of estimating integrated circuit yield comprises providing anintegrated circuit layout and a set of systematic defects based on amanufacturing process. Next, the method represents a systematic defectby modifying structures in the integrated circuit layout to createmodified structures. More specifically, for defects causing a shortcircuit, the method pre-expands the structures when the structurescomprise a higher systematic defect sensitivity level, and pre-shrinksthe structures when the structures comprise a lower systematic defectsensitivity level. As described above, the amount by which a shape edgeis shrunk or expanded is arrived at by calibration of this technique onwith yield data from the target manufacturing process, using carefullyconstructed test structures.

Following this, a critical area analysis is performed on the integratedcircuit layout using the modified structures, wherein dot-throwing,geometric expansion, or Voronoi diagrams are used. As described above,in the dot-throwing example, minimum-width, minimum-space M3 lines lyingabove wide M2 lines are prone to shorting because of CMP problems. Toaccount for this process sensitivity, the edges of these M3 lines areexpanded by a small amount where they lie above the wide M2 wires. Then,the CAA is performed to measure M3 shorts. Dots are more likely to causeshorts in the M3 lines that are expanded, which matches the fact thatthese lines are more sensitive to shorts for reasons other than justcritical area.

The method then computes a fault density value, wherein random defectsand systematic defects are computed. The fault density value issubsequently compared to a predetermined value, wherein thepredetermined value is determined using test structures and/or yielddata from a target manufacturing process. As described above, if thefailure rate for a given systematic defect is reported inparts-per-million (PPM), then the weighted-CAA technique is applied tothe test layout using different expansion/shrink amounts until theoverall fault density—for random defects plus PPM for systematicdefects—matches the predicted fault density for the test data.

In other words, the method estimates integrated circuit yield, whereinthe method identifies shape configurations susceptible to systematicdefects and measures the sensitivity of the shape configurations to thesystematic defects. Next, layout edges of the shape configurations arepre-expanded or pre-shrunk based on the sensitivity to the systematicdefects. More specifically, for short-circuit-causing defects, thelayout edges are pre-expanded when the shape configurations comprise ahigher systematic defect sensitivity level, and pre-shrunk when theshape configurations comprise a lower systematic defect sensitivitylevel. And for open-circuit-causing defects, the layout edges arepre-shrunk when the shape configurations comprise a higher systematicdefect sensitivity and pre-expanded when the shape configurationscomprise a lower systematic defect sensitivity level. Again, asdescribed above, the amount by which a shape edge is shrunk or expandedis arrived at by calibration of this technique on with yield data fromthe target manufacturing process, using carefully constructed teststructures.

A critical area analysis is then performed, comprising computing a faultdensity value. Specifically, dot-throwing, geometric expansion, orVoronoi diagrams are used to perform the critical area analysis. Forexample, as described above, the geometric-expansion technique involvesrepeated shape expansion of prescribed sizes. It's then natural tocombine the expansion or shrinking of certain shapes with the overallexpansion used in this technique.

Further, the computing of the fault density value computes randomdefects and systematic defects. Following this, a size of the shapeconfigurations are modified until the fault density value matches apredicted fault density, wherein the predicted fault density isdetermined using test structures and/or yield data from a targetmanufacturing process. As described above, if the failure rate for agiven systematic defect is reported in parts-per-million (PPM), then theweighted-CAA technique is applied to the test layout using differentexpansion/shrink amounts until the overall fault density—for randomdefects plus PPM for systematic defects—matches the predicted faultdensity for the test data.

Another method of embodiments herein estimates integrated circuit yield,wherein the method provides an integrated circuit layout, test sitedata, and a set of systematic defects based on the test site data. Next,the method assigns each set of systematic defects a weight based on aprobability of a fault occurring, wherein the weight is derived fromtest structures in technology test sites. As described above, thisapproach does not rely on shrinking or expanding shapes prior to theCAA, but rather to assigning weights to certain geometricconfigurations.

Following this, a critical area analysis is performed on the integratedcircuit layout using the weight, wherein the critical area analysisperforms dot-throwing, geometric expansion, or using Voronoi diagrams.As described above, in the case of a dot-throwing approach to the CAA,it is known for each dot the shape(s) for which the defect occurs. As anadditional input the increased probability of a fault for certainpatterns is needed. If for example a short between adjacent minimum anda wide wires occurs with a higher probability, the contribution of thisdot is multiplied to the CAA by the appropriate weight.

The method then computes a fault density value, wherein random defectsand systematic defects are computed. Subsequently, the fault densityvalue is compared to a predetermined value. As described above, thecalibrated technique is then applied to general layouts.

In other words, the method estimates integrated circuit yield, whereinthe method derives weighting factors for geometric configurations on anintegrated circuit, wherein the weighting factors represent increasedprobabilities of a fault occurring at the geometric configurations. Theweighting factors are derived from test structures in technology testsites. Next, one of the weighting factors is assigned to each of thegeometric configurations based on the increased probabilities of thefault occurring at the geometric configurations. Again, as describedabove, this approach does not rely on shrinking or expanding shapesprior to the CAA, but rather to assigning weights to certain geometricconfigurations.

Following this, a critical area analysis is performed using theweighting factors, wherein a fault density value is computed. Thecritical area analysis is performed using dot-throwing, geometricexpansion, or Voronoi diagrams. As described above, in the case of aVoronoi CAA, a Voronoi polygon involving adjacent minimum-width and widewires is given additional weight when its critical-area contribution istallied. Moreover, the computing of the fault density value computesrandom defects and systematic defects. As described above, thecalibrated technique is then applied to general layouts.

FIG. 1 illustrates a flow diagram of a method for estimating integratedcircuit yield. The method begins by identifying shape configurationssusceptible to systematic defects (item 100) and measures thesensitivity of the shape configurations to the systematic defects (item110). Next, layout edges of the shape configurations are pre-expanded orpre-shrunk based on the sensitivity to the systematic defects (item120). More specifically, the layout edges are pre-expanded when theshape configurations comprise a higher systematic defect sensitivitylevel for shorts and a lower systematic defect sensitivity level foropens (item 122); and, pre-shrunk when the shape configurations comprisea lower systematic defect sensitivity level for shorts and a highersystematic defect sensitivity level for opens (item 124). As describedabove, the amount by which a shape edge is shrunk or expanded is arrivedat by calibration of this technique on with yield data from the targetmanufacturing process, using carefully constructed test structures.

A critical area analysis is then performed, comprising computing a faultdensity value (item 130). Specifically, dot-throwing, geometricexpansion, or Voronoi diagrams are used to perform the critical areaanalysis (item 132). For example, as described above, thegeometric-expansion technique involves repeated shape expansion ofprescribed sizes. It's then natural to combine the expansion orshrinking of certain shapes with the overall expansion used in thistechnique.

Further, the computing of the fault density value computes randomdefects and systematic defects (item 134). Following this, a size of theshape configurations are modified until the fault density value matchesa predicted fault density (item 140), wherein the predicted faultdensity is determined using test structures and/or yield data from atarget manufacturing process (item 142). As described above, if thefailure rate for a given systematic defect is reported inparts-per-million (PPM), then the weighted-CAA technique is applied tothe test layout using different expansion/shrink amounts until theoverall fault density—for random defects plus PPM for systematicdefects—matches the predicted fault density for the test data.

FIG. 2 illustrates a flow diagram for another method of estimatingintegrated circuit yield. The method begins by deriving weightingfactors for geometric configurations on an integrated circuit, whereinthe weighting factors represent increased probabilities of a faultoccurring at the geometric configurations (item 200). The weightingfactors are derived from test structures in technology test sites (item202). Next, one of the weighting factors is assigned to each of thegeometric configurations based on the increased probabilities of thefault occurring at the geometric configurations (item 210). As describedabove, this approach does not rely on shrinking or expanding shapesprior to the CAA, but rather to assigning weights to certain geometricconfigurations.

Following this, a critical area analysis is performed using theweighting factors, wherein a fault density value is computed (item 220).The critical area analysis is performed using dot-throwing, geometricexpansion, or Voronoi diagrams (item 222). As described in theCMP-relegated example above, in the case of a Voronoi CAA, a Voronoipolygon involving adjacent minimum-width and wide wires is givenadditional weight when its critical-area contribution is tallied.Moreover, the computing of the fault density value computes randomdefects and systematic defects (item 224). As described above, thecalibrated technique is then applied to general layouts.

Accordingly, embodiments herein use the basic framework of critical areaanalysis to measure sensitivity to random defects. To account forsystematic defects, the analysis is modified by giving higher weight tothe faults involving geometric configurations that are known to besensitive in the manufacturing process. The computed value from thisprocedure is thus no longer simply critical area but is instead weightedcritical area.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the invention that others can, by applyingcurrent knowledge, readily modify and/or adapt for various applicationssuch specific embodiments without departing from the generic concept,and, therefore, such adaptations and modifications should and areintended to be comprehended within the meaning and range of equivalentsof the disclosed embodiments. It is to be understood that thephraseology or terminology employed herein is for the purpose ofdescription and not of limitation. Therefore, while the embodiments ofthe invention have been described in terms of preferred embodiments,those skilled in the art will recognize that the embodiments of theinvention can be practiced with modification within the spirit and scopeof the appended claims.

1. A method of estimating integrated circuit yield, comprising:identifying shape configurations susceptible to systematic defects;measuring sensitivity of said shape configurations to said systematicdefects; one of pre-expanding and pre-shrinking layout edges of saidshape configurations based on said sensitivity to said systematicdefects; performing a critical area analysis, comprising computing afault density value; and modifying a size of said shape configurationsuntil said fault density value matches a predicted fault density.
 2. Themethod according to claim 1, wherein said one of said pre-expanding andsaid pre-shrinking of said layout edges comprises pre-expanding whensaid shape configurations comprise a higher systematic defectsensitivity level.
 3. The method according to claim 1, wherein said oneof said pre-expanding and said pre-shrinking of said layout edgescomprises pre-shrinking when said shape configurations comprise a lowersystematic defect sensitivity level.
 4. The method according to claim 1,further comprising determining said predicted fault density using atleast one of test structures and yield data from a target manufacturingprocess.
 5. The method according to claim 1, wherein said performing ofsaid critical area analysis comprises performing one of dot-throwing,geometric expansion, and using Voronoi diagrams.
 6. The method accordingto claim 1, wherein said computing of said fault density value comprisescomputing random defects and systematic defects.
 7. A method ofestimating integrated circuit yield, comprising: deriving weightingfactors for geometric configurations on an integrated circuit, derivingweighting factors that represent increased probabilities of a faultoccurring at said geometric configurations; assigning one of saidweighting factors to each of said geometric configurations based on saidincreased probabilities of said fault occurring at said geometricconfigurations; performing a critical area analysis using said weightingfactors, comprising computing a fault density value.
 8. The methodaccording to claim 7, wherein said deriving of said weighting factorscomprises deriving said weighting factors from test structures intechnology test sites.
 9. The method according to claim 7, wherein saidperforming of said critical area analysis comprises performing one ofdot-throwing, geometric expansion, and using Voronoi diagrams.
 10. Themethod according to claim 7, wherein said computing of said faultdensity value comprises computing random defects and systematic defects.11. A method of estimating integrated circuit yield, comprising:identifying shape configurations susceptible to systematic defects;measuring sensitivity of said shape configurations to said systematicdefects; one of pre-expanding and pre-shrinking layout edges of saidshape configurations based on said sensitivity to said systematicdefects; performing a weighted critical area analysis based on aprobability of a defect occurring in said shape configurations,comprising computing a fault density value; and modifying a size of saidshape configurations until said fault density value matches a predictedfault density.
 12. The method according to claim 11, wherein said one ofsaid pre-expanding and said pre-shrinking of said layout edges comprisespre-expanding when said shape configurations comprise a highersystematic defect sensitivity level.
 13. The method according to claim11, wherein said one of said pre-expanding and said pre-shrinking ofsaid layout edges comprises pre-shrinking when said shape configurationscomprise a lower systematic defect sensitivity level.
 14. The methodaccording to claim 11, further comprising determining said predictedfault density using at least one of test structures and yield data froma target manufacturing process.
 15. The method according to claim 11,wherein said performing of said weighted critical area analysiscomprises performing one of dot-throwing, geometric expansion, and usingVoronoi diagrams.
 16. The method according to claim 11, wherein saidcomputing of said fault density value comprises computing random defectsand systematic defects.
 17. The method according to claim 7, whereinsaid performing a weighted critical area analysis comprises derivingweighting factors from test structures in technology test sites.